Yang Si-Wei, Xie Yuhao, Liu Jia-Zhen, Zhang De, Huang Jie, Liang Pei
College of Optical and Electronic Technology, China Jiliang University, Hangzhou 310018, China.
College of Horticulture & Forestry Sciences, Key Laboratory of Horticultural Plant Biology, Ministry of Education, Huazhong Agricultural University, Wuhan 430070, China.
Spectrochim Acta A Mol Biomol Spectrosc. 2024 Sep 5;317:124427. doi: 10.1016/j.saa.2024.124427. Epub 2024 May 10.
The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.
混合溶液的识别是化学分析中一个具有挑战性且重要的课题。在本文中，我们提出了一种新颖的工作流程，可实现对混合溶液的快速定性和定量检测。我们以甲醇 - 乙醇混合溶液为例来证明此工作流程的优越性。该工作流程包括以下步骤：（1）通过连续小波变换（CWT）将拉曼光谱转换为拉曼图像；（2）使用MobileNetV3作为骨干网络，改进的多标签和多通道同步能够同时预测多种混合物浓度；（3）使用迁移学习和多阶段训练策略进行训练以实现准确的定量分析。我们比较了六种传统机器学习算法和两种深度学习模型来评估我们新方法的性能。实验结果表明，我们的模型在预测甲醇和乙醇浓度时取得了良好的预测结果，决定系数R大于0.999。在不同浓度下，平均绝对百分比误差（MAPE）和相对标准偏差（RSD）均优于其他模型，这表明我们的工作流程具有出色的分析能力。重要的是，我们解决了当前拉曼光谱定量分析算法几乎无法同时准确预测多种物质浓度的问题。总之，可以预见，这种无损、自动化且高度准确的工作流程能够进一步推动拉曼光谱技术的发展。
